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- PublicationAccès libreReal-time Environmental Monitoring for Cloud-based Hydrogeological Modeling with HydroGeoSphereThis paper describes an architecture for real-time environmental modeling. It consists of a wireless mesh network equipped with sensors and a cloud-based infrastructure to perform real-time environmental sim- ulations using a physics-based model combined with an Ensemble Kalman Filter. The purpose of the system is to optimize groundwater abstraction close to a river. These initial studies demonstrate that the cloud infrastructure can simultaneously compute a large number of simula- tions, thus allowing for the implementation of Ensemble Kalman Filters in real-time.
- PublicationAccès libreWireless Mesh Networks and Cloud Computing for Real Time Environmental SimulationsPredicting the influence of drinking water pumping on stream and groundwater levels is essential for sustainable water management. Given the highly dynamic nature of such systems any quantitative analysis must be based on robust and reliable modeling and simulation approaches. The paper presents a wireless mesh-network framework for environmental real time monitoring integrated with a cloud computing environment to execute the hydrogeological simulation model. The simulation results can then be used to sustainably control the pumping stations. The use case of the Emmental catchment and pumping location illustrates the feasibility and effectiveness of our approach even in harsh environmental conditions.
- PublicationMétadonnées seulementCloudification of a Legacy Hydrological Simulator using Apache SparkThe field of hydrology usually relies on complex multiphysics systems and data collected from geographically distributed sensors in order to obtain good quality predictions and analysis of how wa- ter moves through the environment. Nowadays, the computational resources needed to run such com- plex simulators, and the increasing size of datasets related to the models have arisen an interest to- wards distributed infrastructures like clouds. This paper presents the results of applying a cloudifica- tion methodology to a legacy hydrological simulator (HydroGeoSphere), wrapped with an ensemble Kal- man filter. This work describes how the methodology was applied, the particularities of its implementation and configuration for the Apache Spark iterative map- reduce platform, and the results of an evaluation in a commodity cluster against an MPI implementation of the simulator.